CRCNS Research Proposal:Topological and Dynamical Structures of Brain Development and Sexual-Dimorphism in C. Elegans

CRCNS 研究计划：线虫大脑发育和性别二态性的拓扑和动力学结构

• 批准号: 1912194
• 负责人: Raul Rabadan
• 金额: $ 100万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912194&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Topological; Dynamical

The development of the nervous system, specifically the dynamics of neuronal development and wiring to build brain architecture and their constructive role in emergent brain activity, constitutes a central unexplained phenomenon in living systems. The study of developing brains requires a comprehensive and systematic characterization of the brain of an organism at different ages and a suitable mathematical framework, able to capture the structure of the growing nervous system and the emerging networks therein. We propose to address this fundamental challenge by developing such a mathematical framework capable of characterizing underlying network changes in living brains and their consequences for functional neural activity and resulting behavior. This mathematical framework will be applied to analyze the complete nervous system, at single-cell precision, of the model organism C. elegans. To address these important challenges, we have assembled an interdisciplinary team with expertise in topology, computational biology, statistics, theoretical physics, neuroscience and biology of the model organism. Our group will develop new mathematical, statistical, and computational tools to characterize the structure of developing brain networks. This analysis will reveal shared-organizational, emergent principles of nervous-system development and function. Based on the widespread representation of biological data as complex networks and the universality of the mathematical, statistical, and computational methods we will develop, we expect wide applicability beyond the original system.The aforementioned approach will be led by experiments that aim at providing multiple views of a developing network and their functional consequences to whole-brain activity. We will analyze the brain at two levels: changes to the underlying network as a consequence of extensive neural additions and connective neural (re-)wiring. We will compare the developing network at two transition periods: early maturation from the first to the second larval stage and, later, maturation of the two different sexes. In both of these developmental periods, newborn neurons grow the existing brain network, considerably, by roughly a third in size. In order to characterize the global properties of the data collected from these two different layers (neural network and brain activity) and to study the maps between them, we will develop tools based on topological data analysis (TDA) and Bayesian inference techniques. TDA provides methodology derived from algebraic topology that can be used to extract global features in large datasets. As a relatively new field, there are several major roadblocks that obstruct the wide applicability of TDA to biological systems, including the development of statistical approaches, comparison (homomorphisms) of networks (simplicial complexes), and time-series analysis. These tools will be then applied to study biological datasets that describe the developing brain network and changes to neurobehavioral activity therein. In particular, we will characterize basal networks and those for attractive and aversive behavior, for whole brains at a single-cell level, during developmental transitions that are known to restructure this behavioral network at both the level of input and output.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

神经系统的发育，特别是神经元发育和布线的动力学，以建立大脑结构及其在大脑活动中的建设性作用，构成了生命系统中无法解释的中心现象。大脑发育的研究需要对不同年龄的生物体大脑进行全面和系统的表征，并需要一个合适的数学框架，能够捕捉生长中的神经系统的结构和其中新兴的网络。我们建议通过开发这样一个数学框架来解决这一根本性挑战，该框架能够表征活体大脑中的潜在网络变化及其对功能性神经活动和由此产生的行为的影响。这个数学框架将被应用于分析完整的神经系统，在单细胞精度，模式生物C。优美的为了应对这些重要的挑战，我们组建了一个跨学科团队，拥有拓扑学，计算生物学，统计学，理论物理学，神经科学和模式生物学方面的专业知识。我们的团队将开发新的数学，统计和计算工具来表征发育中的大脑网络的结构。这种分析将揭示神经系统发育和功能的共享组织、涌现原则。基于生物学数据作为复杂网络的广泛代表性以及我们将开发的数学、统计和计算方法的普遍性，我们期望超越原始系统的广泛适用性。上述方法将由旨在提供发展中网络的多个视图及其对全脑活动的功能后果的实验主导。我们将在两个层面上分析大脑：作为广泛神经添加和连接神经（重新）布线的结果的底层网络的变化。我们将比较两个过渡时期的发展网络：从第一个到第二个幼虫阶段的早期成熟，后来，两个不同性别的成熟。在这两个发育阶段，新生神经元会大大增加现有的大脑网络，大约增加三分之一的大小。为了表征从这两个不同层（神经网络和大脑活动）收集的数据的全局属性，并研究它们之间的映射，我们将开发基于拓扑数据分析（TDA）和贝叶斯推理技术的工具。TDA提供了从代数拓扑中衍生的方法，可用于在大型数据集中提取全局特征。作为一个相对较新的领域，有几个主要的障碍阻碍了TDA对生物系统的广泛适用性，包括统计方法的发展，网络（单纯形复合体）的比较（同态）和时间序列分析。然后，这些工具将被应用于研究描述发育中的大脑网络及其神经行为活动变化的生物数据集。特别是，我们将在单细胞水平上对整个大脑的基础网络以及吸引和厌恶行为的网络进行表征，并在已知的输入和输出水平上重建该行为网络的发育过渡期间。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Stability of 2-Parameter Persistent Homology

• DOI: 10.1007/s10208-022-09576-6
• 发表时间: 2022-10-17
• 期刊: FOUNDATIONS OF COMPUTATIONAL MATHEMATICS
• 影响因子: 3
• 作者: Blumberg, Andrew J.;Lesnick, Michael
• 通讯作者: Lesnick, Michael

Neuron matching in C. elegans with robust approximate linear regression without correspondence

• DOI: 10.1109/wacv48630.2021.00288
• 发表时间: 2021-01
• 期刊: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
• 作者: Amin Nejatbakhsh;E. Varol

Molecular topography of an entire nervous system.

• DOI: 10.1016/j.cell.2021.06.023
• 发表时间: 2021-08-05
• 期刊: Cell
• 影响因子: 64.5
• 作者: Taylor SR;Santpere G;Weinreb A;Barrett A;Reilly MB;Xu C;Varol E;Oikonomou P;Glenwinkel L;McWhirter R;Poff A;Basavaraju M;Rafi I;Yemini E;Cook SJ;Abrams A;Vidal B;Cros C;Tavazoie S;Sestan N;Hammarlund M;Hobert O;Miller DM 3rd
• 通讯作者: Miller DM 3rd

Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images.

• DOI: 10.1007/978-3-030-87237-3_45
• 发表时间: 2021-09
• 期刊: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
• 作者: Rao BY;Peterson AM;Kandror EK;Herrlinger S;Losonczy A;Paninski L;Rizvi AH;Varol E
• 通讯作者: Varol E

Efficient Approximation of Multiparameter Persistence Modules

多参数持久性模块的高效逼近

• 发表时间: 2022
• 期刊: ArXivorg
• 作者: David Loiseaux, Mathieu Carrière